There is huge opportunity buried in big data, the challenge is how to realize actionable insights from huge amounts of disparate data. Most organizations realize the potential but have not yet figured out how to tap into Big Data.  Many industries by their own admission are still coming to terms with it. The challenge now is to figure out how organizations will actually use data in practical terms.

Providing operational insights and innovative solutions to enduring challenges and opportunities, Big Data with deep analytics can bring about new ways to transform the organization, its processes, an entire industry and even society. Pushing the boundaries of Big Data analytics uncovers new insights and opportunities. Big data is not just “big.” The growing volume of data is only one of many characteristics that are often associated with Big Data, such as variety, velocity, veracity and others. Despite these great benefits, many Big Data project fails due to the insufficient skills. Big Data project failures typically result from not understanding and carefully planning the process and supporting it with adequate resources and talent.

Why Big Data Projects Fail

Big Data projects fail because many organizations are driving blind with their data and stumble in predictable ways. The most common causes of Big Data project failures are management resistance, complex blend of domain knowledge and lack of right skills, problem avoidance, and Big Data silos.

Those enterprises which lack in understanding the project’s cost of ownership and information technology systems involved – are in a great danger. Most data scientists are focused on discovering new questions and work with BI tool for business related questions and retrieve answers. Data science model involves a degree of sophistication, stability. Those organizations that fail to sufficiently plan and support Big Data projects are setting themselves up for failure.

Big Data project also fails due to network congestion, failing to train personnel in the complexities of Big Data analytics, and unexpected problems beyond data & technology. Exploring data is just a module of a Big Data project and getting access and process to such data is vital. An iterative and small approach over Big Data can help you avoid pitfalls. The problem is often exacerbated if different groups conflict of the value of undertaking Big Data Analysis or other strategic priorities outweighs Big Data required resources. Organizations need to fully support Big Data projects and its goals if they expect to gain actionable insights to improve the organization or offer a better user experience.


Scalable Systems big data methodology is an enterprise-class offering that provides you to store, manage and analyze your data, build and deliver next-generation applications and services. Our methodology includes harnessing an enterprise’s existing IT in order to design a solution that combines cost savings with rapid development and deployment.